A. Field of the Invention
The method described in this invention is an integrated approach to minimizing expected, controllable unit costs during drilling operations by optimizing real-time values of operating variables. The term "expected, controllable unit costs" refers to costs that will likely be incurred during drilling over a future interval of a well and which can be affected by decisions made by drilling personnel.
The invention is based on circumstances generally encountered during drilling operations for hydrocarbons. However, the techniques may be applied to other mining, drilling, or similar capital intensive industrial operations in which real-time optimization of operating variables can result in cost reductions.
The total cost of drilling a well is a function of the time required for actual drilling operations in addition to the time allocated to non-drilling activities such as, for example, well logging and setting casing. In order to minimize total drilling costs, it is necessary to minimize the time required for actual drilling operations. The speed at which drilling operations proceed is commonly measured by the penetration rate of the bit. If the penetration rate can be optimized over a future drilling interval, it is likely that drilling-related costs can also be optimized over the same interval.
During drilling operations, the values of certain drilling variables are usually chosen by operating personnel who rely primarily on experience and historical data. In some cases the values they select are close to the optimal ones; however, the absence of a real-time, predictive, quantifiable methodology usually results in selections which only coincidentially represent the optimal values.
The purpose of the optimization method described in this invention is to identify values of selected operating variables that will minimize expected, controllable unit costs over a future drilling interval. In the normal operating mode, this optimization involves a simulation of weight on bit and rotary speed since these two variables are generally considered the most important "controllable" variables. Other variables, however, can also be simulated. In addition, zones of different lithologies represented by different sets of drilling data can be simulated to examine the effects on the expected, controllable unit costs.
A successful optimization of expected, controllable unit costs depends on a valid simulation of operating variables which, in turn, is a function of an accurate predictive penetration rate model. The penetration rate model given in this invention is a multivariate model that consists of drilling variables that are known to have a significant effect on the penetration rate of a bit under real-time conditions (see FIG. 1).
The method described in this invention utilizes a predictive variance statistic which quantifies data quality and insures that the penetration rate model is based on the best available predictor variables. With this statistic, variables that may be quantified through technological advances may be evaluated for inclusion in the penetration rate model.
The invention also describes an expression for calculating expected, controllable unit costs whereby both bit costs and rig operating costs are prorated over a future drilling interval.
B. Description of the Prior Art
The importance of establishing the correct values for controllable drilling variables has been recognized for some time, and optimization attempts have been made in the past. However, these efforts have not been successful for several reasons including the following: (1) they were based on incomplete or inaccurate penetration rate models, or (2) they were static, i.e., they used constants that were not always applicable to the given well, or (3) time constraints did not permit all the required calculations to be performed before drilling conditions changed, and (4) bit costs and tripping costs were incorrectly prorated. Given these and other shortcomings, real-time optimization models have not gained acceptance in field applications.
Several authors have studied the effects on the penetration rate of individual drilling variables including weight on bit, rotary speed, bit hydraulics, and drilling fluid properties. In virtually all these studies, the effect of an individual variable on the penetration rate was examined while holding all other variables constant. While informative, the conclusions derived from these studies could not be directly applied in the field since drilling variables are dynamic under real-time conditions. An example of a single variable study is one that was conducted by Eckel who correlated the penetration rate with a Reynolds number function based on bit hydraulics.
Only in the last two decdes have efforts been made to develop a comprehensive model of the penetration rate pattern. M. G. Bingham developed one of the first such models, but it only included formation properties, weight on bit, and rotary speed. Not only were other important drilling variables omitted, but no attempt was made to insure that the model was applicable under a variety of drilling conditions or in different formations. Other authors have proposed methods that may be operable if real-time data on the type of lithology encountered are available.
U.S. Pat. No. 4,407,017 of Zhilikov, Motsokhein, and Parfenov describes an automatic method for controlling drilling variables based on an adaptive mathematical model that considers only the penetration rate, weight on bit, and rotary speed. This method is based on separating the drilling operation into a trial mode and a drilling mode proper. During the trial mode, coefficients of weight on bit and rotary speed are calculated and if they are constant during a six-spot trial, are used to generate adaptive signals that automatically adjust the weight on bit and rotary speed during the drilling mode. This method does not incorporate important drilling variables and mechanisms other than weight on bit and rotary speed nor does it recognize that changes in lithology may impact the weight on bit and rotary speed coefficients if they are calculated by the method described in the invention.
The translation of drilling engineering models into effective, real-time decision models based on expected, controllable costs has not been conducted properly in previous studies. Other authors have proposed models based on historical costs incurred during prior drilling intervals, as opposed to models based on expected costs likely to occur over future drilling intervals.
In addition, other authors have not differentiated between total costs and controllable costs. Total costs include all drilling-related costs such as drill pipe, casing, mud, logging services, site preparation, etc. These costs are significant, but due to their quasi-fixed nature, cannot be appreciably changed in the short run. Even when otpimization of these costs is feasible, it is not easily performed on a real-time basis.
In contrast to the quasi-fixed aspect of total costs, controllable costs can be altered in the short run. As used in the present method, controllable costs are future costs that can be optimized in real-time.